Code review, debugging, architecture, testing and docs. 13 reviewed prompts for engineering.
Turns rough testing notes into clear, reproducible bug reports with steps, expected and actual results, severity and suggested checks. Use it after a test session so developers can act without follow-up.
Turns a piece of code into a list of test cases plus runnable test code, with explanations a junior developer can learn from. Use it when you add or change a function and want solid coverage.
A way of working for any code change: plan first, read before editing, change only what is needed, test, check security basics and summarise. Use when writing, fixing or refactoring code.
Reviews a code change against its requirements and lists concrete defects by severity, each with a trigger scenario, fix and test. Use before merging or releasing.
Designs a practical testing strategy for an application: risk map, test layers with targets, dependency handling, the first ten tests to write, CI gates and a four-week rollout. Use it when a suite is missing, slow or untrusted.
Builds a practical test plan for an AI assistant or automated workflow: realistic test cases, pass/fail rules, metrics and review triggers. Use before rolling a workflow out to a team.
Explains a security finding from a code scanner, fixes it in the context of your code, and proposes tests that prove nothing broke. Use when a scan flags unsafe handling of user input.
Designs how to test an AI-powered feature before and after launch: success metrics, a balanced test set, a 1-5 scoring rubric with examples, pass criteria and a failure log. Use when you need to know whether an AI feature is good enough to ship.
Produces a readable set of unit tests with a coverage map from a Python snippet and its intended behavior. Use it when adding tests to untested or legacy code.
Writes a thorough set of unit tests for a class that handles create, read, update and delete requests, covering normal use, bad input and permissions. Use when you need reliable tests before changing existing code.
Requires real evidence before a software change is reported as finished: build and tests run, the change exercised directly, at least one edge case tried, and results shown. Use when you implement, fix or verify code and can run checks.
Use when you have just written or changed code and before presenting it: runs a focused second pass for the mistakes generated code commonly contains, fixes what it can, and reports what was fixed and what needs the author's attention.
